Selected Variables

base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Previous surgery - LEV
- LGap
- RLL
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 2Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 2Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 2Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 2Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 2Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 2Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
- 2Y. Pelvic Tilt
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
demographic:
- Age
- Gender
- Prior Spine Surgery
- ASA classification
- 3CO
- BMI_First Visit
- Global Tilt
- ideal LL
- Lordosis (top of L1-S1)
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Previous surgery - LEV
- LGap
- RLL
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic Tilt

Propensity Scores Common Support

Model Stats

  • Treatment proportion: 0.127
  • Model Type: elastic_net
  • Accuracy: 0.8965962
  • Params: alpha: 0.2153846 lambda: 0.0142449

Average Treatment Effects - Radiology

Outcome: 6W. Major curve Cobb angle
Distribution:
     0%     25%     50%     75%    100% 
-65.000 -21.000 -10.440  -3.415  16.880 
Model Type Y: boosting 
RMSE: 16.3885663582152 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 12.9342185886316 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -6.369 (Std.Error: 4.34)
Trimmed ATE (Yes-No): -6.261 (Std.Error: 4.522)
Upper ATE (Yes-No): -8.988 (Std.Error: 5.562)
Observational differences in treatment 1.867 (Yes-No) 

   treatment  outcome
1:       Yes 22.89611
2:        No 21.02903
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Major curve Cobb angle
Distribution:
    0%    25%    50%    75%   100% 
-64.00 -22.97  -9.93  -2.28  22.44 
Model Type Y: boosting 
RMSE: 18.7014985353854 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875

Model Type No: boosting 
RMSE: 14.5390640238122 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): 0.763 (Std.Error: 4.145)
Trimmed ATE (Yes-No): 1.048 (Std.Error: 4.304)
Upper ATE (Yes-No): -5.818 (Std.Error: 5.48)
Observational differences in treatment 3.198 (Yes-No) 

   treatment  outcome
1:       Yes 23.67687
2:        No 20.47880
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 2Y. Major curve Cobb angle
Distribution:
    0%    25%    50%    75%   100% 
-60.00 -23.52  -9.53  -1.08  21.99 
Model Type Y: boosting 
RMSE: 19.6485039202452 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 14.4612270116829 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -3.663 (Std.Error: 5.162)
Trimmed ATE (Yes-No): -3.507 (Std.Error: 5.352)
Upper ATE (Yes-No): -6.725 (Std.Error: 6.301)
Observational differences in treatment 1.849 (Yes-No) 

   treatment  outcome
1:       Yes 23.97219
2:        No 22.12335
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-23.631420  -5.244884  -1.457698   2.000000  18.000000 
Model Type Y: boosting 
RMSE: 6.43997981471071 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 5.65914096441526 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): -6.694 (Std.Error: 1.3)
Trimmed ATE (Yes-No): -6.82 (Std.Error: 1.316)
Upper ATE (Yes-No): -3.487 (Std.Error: 4.366)
Observational differences in treatment -0.932 (Yes-No) 

   treatment   outcome
1:       Yes -3.688515
2:        No -2.756435
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-30.098675  -5.534988  -2.000000   1.480410  20.000000 
Model Type Y: boosting 
RMSE: 7.49531258000082 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 5.87961904908195 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -3.655 (Std.Error: 2.081)
Trimmed ATE (Yes-No): -3.589 (Std.Error: 2.188)
Upper ATE (Yes-No): -4.898 (Std.Error: 3.385)
Observational differences in treatment 0.143 (Yes-No) 

   treatment   outcome
1:       Yes -2.502714
2:        No -2.645953
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 2Y. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-31.332362  -5.685001  -1.366539   1.078189  10.268933 
Model Type Y: boosting 
RMSE: 7.62852556132965 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875

Model Type No: boosting 
RMSE: 5.48318578880692 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -6.008 (Std.Error: 1.546)
Trimmed ATE (Yes-No): -6.012 (Std.Error: 1.62)
Upper ATE (Yes-No): -5.946 (Std.Error: 3.184)
Observational differences in treatment -0.997 (Yes-No) 

   treatment   outcome
1:       Yes -3.676968
2:        No -2.680294
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Sagittal Balance
Distribution:
     0%     25%     50%     75%    100% 
-194.79  -68.55  -27.01    2.10  114.15 
Model Type Y: boosting 
RMSE: 49.8876262654283 
Params: nrounds: 100.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 52.3973226579747 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -53.287 (Std.Error: 15.043)
Trimmed ATE (Yes-No): -54.461 (Std.Error: 15.861)
Upper ATE (Yes-No): -29.708 (Std.Error: 30.128)
Observational differences in treatment -8.982 (Yes-No) 

   treatment  outcome
1:       Yes 23.00886
2:        No 31.99110
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Sagittal Balance
Distribution:
     0%     25%     50%     75%    100% 
-237.47  -62.47  -28.13    7.24  109.54 
Model Type Y: boosting 
RMSE: 58.0223445603253 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 51.106528828063 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -45.357 (Std.Error: 14.192)
Trimmed ATE (Yes-No): -44.1 (Std.Error: 14.658)
Upper ATE (Yes-No): -69.328 (Std.Error: 21.342)
Observational differences in treatment -3.004 (Yes-No) 

   treatment  outcome
1:       Yes 34.28567
2:        No 37.29009
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 2Y. Sagittal Balance
Distribution:
      0%      25%      50%      75%     100% 
-252.690  -56.225  -17.085    7.660  107.700 
Model Type Y: boosting 
RMSE: 73.5684668064938 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 52.1538640450979 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -55.217 (Std.Error: 10.547)
Trimmed ATE (Yes-No): -54.356 (Std.Error: 10.892)
Upper ATE (Yes-No): -75.601 (Std.Error: 45.819)
Observational differences in treatment -14.719 (Yes-No) 

   treatment  outcome
1:       Yes 26.24857
2:        No 40.96763
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Global Tilt
Distribution:
      0%      25%      50%      75%     100% 
-68.6200 -18.0175  -6.0000   1.8425  16.0000 
Model Type Y: boosting 
RMSE: 15.8603884346037 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 12.1952879947072 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -13.537 (Std.Error: 4.769)
Trimmed ATE (Yes-No): -13.486 (Std.Error: 4.91)
Upper ATE (Yes-No): -14.72 (Std.Error: 5.747)
Observational differences in treatment -5.812 (Yes-No) 

   treatment  outcome
1:       Yes 18.04944
2:        No 23.86111
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Global Tilt
Distribution:
     0%     25%     50%     75%    100% 
-62.630 -15.715  -5.100   1.000  26.000 
Model Type Y: boosting 
RMSE: 15.8815394939775 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 11.3369466634336 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -15.6 (Std.Error: 2.981)
Trimmed ATE (Yes-No): -15.615 (Std.Error: 3.137)
Upper ATE (Yes-No): -15.28 (Std.Error: 7.357)
Observational differences in treatment -3.087 (Yes-No) 

   treatment  outcome
1:       Yes 22.36419
2:        No 25.45107
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 2Y. Global Tilt
Distribution:
      0%      25%      50%      75%     100% 
-65.2300 -12.6725  -4.0450   2.1525  20.0000 
Model Type Y: boosting 
RMSE: 14.8396483659181 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 10.8552123298433 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -13.608 (Std.Error: 2.921)
Trimmed ATE (Yes-No): -13.469 (Std.Error: 2.916)
Upper ATE (Yes-No): -16.468 (Std.Error: 7.653)
Observational differences in treatment -2.237 (Yes-No) 

   treatment  outcome
1:       Yes 24.46276
2:        No 26.70024
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
    0%    25%    50%    75%   100% 
-65.22 -24.00  -9.38   0.00  29.00 
Model Type Y: boosting 
RMSE: 18.481531218616 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 15.0145925501619 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -8.925 (Std.Error: 5.59)
Trimmed ATE (Yes-No): -8.902 (Std.Error: 5.87)
Upper ATE (Yes-No): -9.493 (Std.Error: 5.346)
Observational differences in treatment -3.333 (Yes-No) 

   treatment   outcome
1:       Yes -52.60333
2:        No -49.27082
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
    0%    25%    50%    75%   100% 
-67.87 -24.56  -7.22   0.00  23.38 
Model Type Y: boosting 
RMSE: 20.1781697031483 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 15.0823332236862 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -12.391 (Std.Error: 7.489)
Trimmed ATE (Yes-No): -12.404 (Std.Error: 7.842)
Upper ATE (Yes-No): -12.081 (Std.Error: 5.642)
Observational differences in treatment 0.311 (Yes-No) 

   treatment   outcome
1:       Yes -48.80097
2:        No -49.11244
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 2Y. Lordosis (top of L1-S1)
Distribution:
      0%      25%      50%      75%     100% 
-65.5600 -22.5050  -8.1800  -0.2375  26.5200 
Model Type Y: boosting 
RMSE: 20.5465751210006 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 14.943125692656 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -10.19 (Std.Error: 5.058)
Trimmed ATE (Yes-No): -10.198 (Std.Error: 5.399)
Upper ATE (Yes-No): -10.034 (Std.Error: 7.886)
Observational differences in treatment -2.919 (Yes-No) 

   treatment   outcome
1:       Yes -51.83774
2:        No -48.91858
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. LGap
Distribution:
      0%      25%      50%      75%     100% 
-64.6206 -24.0000  -9.1254   0.2107  27.3800 
Model Type Y: boosting 
RMSE: 19.6693894753783 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 15.13438406087 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -7.041 (Std.Error: 4.652)
Trimmed ATE (Yes-No): -6.936 (Std.Error: 4.849)
Upper ATE (Yes-No): -9.578 (Std.Error: 4.911)
Observational differences in treatment -4.195 (Yes-No) 

   treatment   outcome
1:       Yes  9.285117
2:        No 13.480212
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. LGap
Distribution:
       0%       25%       50%       75%      100% 
-67.72420 -24.42425  -6.89620   0.58000  22.08000 
Model Type Y: boosting 
RMSE: 21.7975429638111 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 15.2884074938048 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -10.667 (Std.Error: 6.576)
Trimmed ATE (Yes-No): -10.581 (Std.Error: 6.811)
Upper ATE (Yes-No): -12.641 (Std.Error: 7.021)
Observational differences in treatment -1.258 (Yes-No) 

   treatment  outcome
1:       Yes 12.69223
2:        No 13.94980
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 2Y. LGap
Distribution:
       0%       25%       50%       75%      100% 
-65.65180 -22.33365  -8.57860  -0.58385  25.09440 
Model Type Y: boosting 
RMSE: 18.9052708924185 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 14.4735715266741 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): -11.022 (Std.Error: 4.653)
Trimmed ATE (Yes-No): -11.053 (Std.Error: 4.855)
Upper ATE (Yes-No): -10.421 (Std.Error: 6.815)
Observational differences in treatment -3.781 (Yes-No) 

   treatment  outcome
1:       Yes 10.30224
2:        No 14.08358
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Pelvic Tilt
Distribution:
      0%      25%      50%      75%     100% 
-36.4100  -8.2575  -2.0000   2.0000  14.4200 
Model Type Y: boosting 
RMSE: 10.705519947725 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 7.6937479159047 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -6.503 (Std.Error: 3.684)
Trimmed ATE (Yes-No): -6.473 (Std.Error: 3.851)
Upper ATE (Yes-No): -7.275 (Std.Error: 5.053)
Observational differences in treatment -4.452 (Yes-No) 

   treatment  outcome
1:       Yes 17.37857
2:        No 21.83019
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Pelvic Tilt
Distribution:
      0%      25%      50%      75%     100% 
-26.6200  -6.8975  -2.0150   1.3925  23.0000 
Model Type Y: boosting 
RMSE: 9.57611025832631 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 6.76718706313625 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -9.522 (Std.Error: 2.1)
Trimmed ATE (Yes-No): -9.818 (Std.Error: 2.199)
Upper ATE (Yes-No): -2.757 (Std.Error: 3.84)
Observational differences in treatment -2.932 (Yes-No) 

   treatment  outcome
1:       Yes 19.57871
2:        No 22.51027
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 2Y. Pelvic Tilt
Distribution:
     0%     25%     50%     75%    100% 
-25.630  -6.000  -1.440   2.595  12.500 
Model Type Y: boosting 
RMSE: 10.114111250265 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 6.49481066983608 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -6.4 (Std.Error: 2.662)
Trimmed ATE (Yes-No): -6.596 (Std.Error: 2.801)
Upper ATE (Yes-No): -2.555 (Std.Error: 3.528)
Observational differences in treatment -1.502 (Yes-No) 

   treatment  outcome
1:       Yes 21.54000
2:        No 23.04153
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'